Machine Learning Engineer

Company Description

About CyberArk:

CyberArk (NASDAQ: CYBR), is the global leader in Identity Security. Centered on privileged access management, CyberArk provides the most comprehensive security offering for any identity – human or machine – across business applications, distributed workforces, hybrid cloud workloads and throughout the DevOps lifecycle. The world’s leading organizations trust CyberArk to help secure their most critical assets. To learn more about CyberArk, visit our CyberArk blogs or follow us on X, LinkedIn or Facebook.

Job Description

The AI, Data & Research unit is at the forefront of CyberArk’s innovation, building data-driven, ML-powered, and intelligent security solutions. We are looking for a passionate Machine Learning Engineer to join our team of seasoned ML engineers.

You will play a critical role in building a multi-tenant PaaS for ML pipelines and inference, ensuring scalability, reliability, and security. You will take ownership of critical platform components, drive best practices, and mentor other engineers.

• Design, build, and maintain infrastructure-as-code using Python and AWS services for deployment.

• Architect, build, and manage Docker-based services.

• Lead the design and implementation of solutions using AWS services such as SageMaker, Lambda, Step Functions, SageMaker Pipelines, Batch Transform, and Real-Time Endpoints.

• Enhance and maintain CI/CD pipelines (Jenkins and shared libraries).

• Ensure multi-tenant security and tenant isolation across the platform.

• Define and implement observability and monitoring practices with Datadog and other tools.

• Collaborate closely with Data Scientists, Data engineers, MLEs, Product Managers, and other engineering teams to integrate ML workflows.

• Mentor junior engineers and promote engineering best practices.

#LI-Hybrid

#LI-OS1

Qualifications

• Bachelor’s degree in computer science, Software Engineering, or a related field.

• 4+ years of hands-on development experience with Python and AWS.

• Proven experience with infrastructure as code (preferably AWS CDK, Terraform, or CloudFormation).

• Strong knowledge of AWS architecture and services, particularly in data/ML workloads.

• Deep experience with CI/CD pipelines (Jenkins or similar).

• Strong expertise in Docker and containerized applications.

• Demonstrated knowledge of cloud security, scalability, and tenant isolation.

• Hands-on experience with observability platforms (preferably Datadog).

• Self-motivated and goal-oriented with a high work ethic.

Additional Information

• Background in MLOps, Data Platforms, or Machine Learning workflows.

• Experience with additional monitoring and logging tools (CloudWatch, Prometheus, ELK).

• Leadership experience in scaling cloud-native platforms.

• Experience in information security – an advantage

• Understanding of identity & access management, secrets management, or zero-trust architecture - Bonus.